R_Platte_Analysis

To begin, we read and clean the fish data which is stored as “Platte River fish data.xlsx”. Then we create useful categories based on species and segment which we will use in our analyses.

platte_fish <- read_excel("~/Documents/Biology/Biology Research/Platte River/Platte River fish data.xlsx")


#cleaning the No Fish, and Net did not Fish from the data (999 and 998)
platte_fish_cleaned <- platte_fish %>%
  select(Segment, Year, Month, Day, Length, Weight, Species, Count, Gear) %>% #might add in Size
  filter(!Species %in% c(999,998,997,NA)) %>%
  mutate(Count = as.numeric(as.character(Count))) %>%
  mutate(Length = as.numeric(as.character(Length))) %>%
  mutate(Weight = as.numeric(as.character(Weight)))

platte_fish_extended <- platte_fish_cleaned %>%
  mutate(
    Origin = case_when(
      Species %in% c(176, 178, 180, 181, 182) ~ "Non",  # Assign "Non" to the specified species codes
      TRUE ~ "Nat"  # Assign "Nat" to all other species
    )
  )


LLP_fish <- platte_fish_extended %>%
  filter(Segment == "LLP")

CP_fish <- platte_fish_extended %>%
  filter(Segment == "CP")

ULP_fish <- platte_fish_extended %>%
  filter(Segment == "ULP")

ELK_fish <- platte_fish_extended %>%
  filter(Segment == "ELK")

PMC_fish <- platte_fish_extended %>%
  filter(Segment == "PMC")

# Creating the dataset for native fish
native_fish <- platte_fish_extended %>%
  filter(Origin == "Nat") %>%
  filter(Segment != "NA")

# Creating the dataset for non-native fish
nonnative_fish <- platte_fish_extended %>%
  filter(Origin == "Non") %>%
  filter(Segment != "NA")

#separating out all the differet types of sturegeon
sturgeon <- platte_fish_cleaned %>%
  filter(Species %in% c(20,24,26,27,28))


#separates just the endangered pallid sturgeon
pallid_sturgeon <- platte_fish_cleaned %>%
  filter(Species == 26)



#separates just the catfish
catfish <- platte_fish_cleaned %>%
  filter(Species %in% c(300,350,355,360,365,370))

#separates just the invasive carp (grass carp 176, common carp 178, silver carp 180, "Unidentified Asian Carp" 181, Bighead carp 182)
carp <- platte_fish_cleaned %>%
  filter(Species %in% c(176,178,180,181,182))

year_range <- range(platte_fish_cleaned$Year)

unique_species <- unique(platte_fish$Species)
unique_months <- unique(platte_fish$Month)
unique_years <- unique(platte_fish$Year)

# Ensure the directory exists
if (!dir.exists("data")) dir.create("data")

write.csv(platte_fish_cleaned, "data/Platte_Fish_Cleaned.csv", row.names=F) #removes proname column

Here we are converting this data into a format that the “vegan” package in R can use to calculate diversity indices.

dat <- read.csv("/Users/robin/Documents/GitHub/R_Project_2023/BIOS967_Rodenburg/data/Platte_Fish_Cleaned.csv")

fish_vegan <- dat %>% #select(-X) %>%
  filter(Segment != "NA") %>%
  group_by(Segment, Year, Month, Day, Species) %>%
  summarise(count_use=sum(Count)) %>%
  mutate(sample_id=paste(Segment, Year, Month, Day, sep="-")) %>%
  pivot_wider(id_cols=sample_id, names_from=Species, values_from=count_use)

fish_vegan[is.na(fish_vegan)] <- 0 #converts NA's to 0s
#View(fish_vegan)

numeric_data <- fish_vegan %>% select(-sample_id) #remove non-integer column
#numeric_data <- fish_vegan %>% select_if(is.numeric) #also removes any non-integer columns
#print(fish_vegan)

native_fish_vegan <- native_fish %>% 
  group_by(Segment, Year, Month, Day, Species) %>%
  summarise(count_use=sum(Count)) %>%
  mutate(sample_id=paste(Segment, Year, Month, Day, sep="-")) %>%
  pivot_wider(id_cols=sample_id, names_from=Species, values_from=count_use)

nonnative_fish_vegan <- nonnative_fish %>% 
  group_by(Segment, Year, Month, Day, Species) %>%
  summarise(count_use=sum(Count)) %>%
  mutate(sample_id=paste(Segment, Year, Month, Day, sep="-")) %>%
  pivot_wider(id_cols=sample_id, names_from=Species, values_from=count_use)

native_fish_vegan[is.na(native_fish_vegan)] <- 0 #converts NA's to 0s
nonnative_fish_vegan[is.na(nonnative_fish_vegan)] <- 0 #converts NA's to 0s

native_numeric_data <- native_fish_vegan %>% select(-sample_id) #remove non-integer column
nonnative_numeric_data <- nonnative_fish_vegan %>% select(-sample_id) #remove non-integer column

#print(nonnative_fish_vegan)
#print(nonnative_numeric_data)

In this chunk, we are calculating diversity indeces and creating a histogram of the most common shannon diversity for each day of collection. Zero being the most prominent shannon diveristy indicates that most sampling days, only one species was found. This code also breaks the fish data into each season (Spring, Summer, Fall). This wasn not used later in the analyses, but could be useful.

# Calculate Chao1 using the specpool() function
chao1_values <- specpool(numeric_data)$Chao1
native_chao1_values <- specpool(native_numeric_data)$Chao1
nonnative_chao1_values <- specpool(nonnative_numeric_data)$Chao1

# If the specpool() function doesn't work, you can alternatively try this:
# chao1_values <- estimateR(numeric_data)[, "Chao1"]

# Calculate other diversity indices
diversity_indices <- list(
  Shannon = diversity(numeric_data, index = "shannon"),
  Simpson = diversity(numeric_data, index = "simpson"),
  InverseSimpson = diversity(numeric_data, index = "invsimpson"),
  FisherAlpha = specnumber(numeric_data) - 1,
  Richness = specnumber(numeric_data),
  Evenness = diversity(numeric_data, index = "shannon") / log(specnumber(numeric_data))
)

native_diversity_indices <- list(
  Shannon = diversity(native_numeric_data, index = "shannon"),
  Simpson = diversity(native_numeric_data, index = "simpson"),
  InverseSimpson = diversity(native_numeric_data, index = "invsimpson"),
  FisherAlpha = specnumber(native_numeric_data) - 1,
  Richness = specnumber(native_numeric_data),
  Evenness = diversity(native_numeric_data, index = "shannon") / log(specnumber(native_numeric_data))
)


nonnative_diversity_indices <- list(
  Shannon = diversity(nonnative_numeric_data, index = "shannon"),
  Simpson = diversity(nonnative_numeric_data, index = "simpson"),
  InverseSimpson = diversity(nonnative_numeric_data, index = "invsimpson"),
  FisherAlpha = specnumber(nonnative_numeric_data) - 1,
  Richness = specnumber(nonnative_numeric_data),
  Evenness = diversity(nonnative_numeric_data, index = "shannon") / log(specnumber(nonnative_numeric_data))
)
# Add Chao1 to the list
diversity_indices$Chao1 <- chao1_values
native_diversity_indices$Chao1 <- chao1_values
nonnative_diversity_indices$Chao1 <- chao1_values

# Convert the list to a dataframe
diversity_data <- as.data.frame(do.call(cbind, diversity_indices))
native_diversity_data <- as.data.frame(do.call(cbind, native_diversity_indices))
nonnative_diversity_data <- as.data.frame(do.call(cbind, nonnative_diversity_indices))

diversity_data_extend <- cbind(diversity_data, sample_id = fish_vegan$sample_id) %>% 
  separate(sample_id, into = c("Segment", "Year", "Month", "Day"), sep = "-") %>%
  filter(Segment != "NA") %>% 
  mutate(Segment = factor(Segment, levels = c("CP", "ULP", "ELK", "LLP", "PMC"))) %>% 
  mutate(Date = as.Date(paste(Year, Month, Day, sep = "-"), format = "%Y-%m-%d")) %>% 
  select(-Year, -Month, -Day)

native_diversity_data_extend <- cbind(native_diversity_data, sample_id = native_fish_vegan$sample_id) %>% 
  separate(sample_id, into = c("Segment", "Year", "Month", "Day"), sep = "-") %>%
  filter(Segment != "NA") %>% 
  mutate(Segment = factor(Segment, levels = c("CP", "ULP", "ELK", "LLP", "PMC"))) %>% 
  mutate(Date = as.Date(paste(Year, Month, Day, sep = "-"), format = "%Y-%m-%d")) %>% 
  select(-Year, -Month, -Day)

nonnative_diversity_data_extend <- cbind(nonnative_diversity_data, sample_id = nonnative_fish_vegan$sample_id) %>% 
  separate(sample_id, into = c("Segment", "Year", "Month", "Day"), sep = "-") %>%
  filter(Segment != "NA") %>% 
  mutate(Segment = factor(Segment, levels = c("CP", "ULP", "ELK", "LLP", "PMC"))) %>% 
  mutate(Date = as.Date(paste(Year, Month, Day, sep = "-"), format = "%Y-%m-%d")) %>% 
  select(-Year, -Month, -Day)
# View the results

#creates a new variable that bunches the samples by season. 
diversity_data_extend <- diversity_data_extend %>%
  arrange(Date) %>%
  mutate(SeasonGroup = cumsum(c(TRUE, diff(Date) > 90))) # assuming a break of more than 90 days indicates a new season

#creates a new variable that bunches the samples by season. 
native_diversity_data_extend <- native_diversity_data_extend %>%
  arrange(Date) %>%
  mutate(SeasonGroup = cumsum(c(TRUE, diff(Date) > 90))) # assuming a break of more than 90 days indicates a new season

#creates a new variable that bunches the samples by season. 
nonnative_diversity_data_extend <- nonnative_diversity_data_extend %>%
  arrange(Date) %>%
  mutate(SeasonGroup = cumsum(c(TRUE, diff(Date) > 90))) # assuming a break of more than 90 days indicates a new season

diversity_data_extend <- diversity_data_extend %>%
  mutate(
    Season = case_when(
      Date >= as.Date(paste(year(Date), "01-01", sep = "-")) & Date <= as.Date(paste(year(Date), "04-30", sep = "-")) ~ "Spring",
      Date >= as.Date(paste(year(Date), "05-01", sep = "-")) & Date <= as.Date(paste(year(Date), "07-31", sep = "-")) ~ "Summer",
      Date >= as.Date(paste(year(Date), "08-01", sep = "-")) & Date <= as.Date(paste(year(Date), "12-31", sep = "-")) ~ "Fall",
      #TRUE ~ NA_character_ # Assigns NA to any dates that don't fit the above criteria
    )
  )

diversity_data_extend <- diversity_data_extend %>%
  mutate(
    Subcategory = case_when(
      Season == "Spring" & month(Date) %in% c(01, 02) ~ "Early",
      Season == "Spring" & month(Date) == 03 ~ "Middle",
      Season == "Spring" & month(Date) == 04 ~ "Late",
      Season == "Summer" & month(Date) == 05 ~ "Early",
      Season == "Summer" & month(Date) == 06 ~ "Middle",
      Season == "Summer" & month(Date) == 07 ~ "Late",
      Season == "Fall" & month(Date) == 08 ~ "Early",
      Season == "Fall" & month(Date) == 09 ~ "Middle",
      Season == "Fall" & month(Date) %in% c(10, 11, 12) ~ "Late",
      TRUE ~ NA_character_
    )
  )


#print(diversity_data_extend)

#PLOTTING A HISTOGRAM OF ALL SHANNON DIVERSITY NUMBERS IN shannon_div
ggplot(data.frame(Shannon_Diversity = diversity_data_extend$Shannon), aes(x = Shannon_Diversity)) +
  geom_histogram(binwidth = 0.1, fill = "dodgerblue", color = "black", alpha = 0.7) +
  labs(title = "Distribution of Shannon Diversity", x = "Shannon Diversity", y = "Number of Samples")

This chunk plots various diversity indices of interest (Richness, Shannon, Simpson). Richness has been scaled by a factor of 10 to fit on the same graph. As can be seen, these indices are highly correlated. For now on, we will focus on Shannon Diversity.

# Filter data for the desired time range (2010-2014)
filtered_data <- diversity_data_extend %>%
  filter(Date >= "2007-01-01" & Date <= "2017-12-31")

diversity_plot <- ggplot(filtered_data, aes(x = Date, group = SeasonGroup)) +
  geom_smooth(aes(y = Shannon, color = "Shannon")) +
  geom_smooth(aes(y = Richness/10, color = "Richness (scaled)")) +
  geom_smooth(aes(y = Simpson, color = "Simpson")) +
  labs(x = "Year", y = "Diversity") +
  ggtitle("Diversity Indeces Over Time")

# Print the plot
print(diversity_plot)

This chunk plots the Native diversity indices over time. As can be seen, it is very similar to the total diversity indices.

#first I have to find the diversity data for just native species!

native_filtered_data <- native_diversity_data_extend %>%
  filter(Date >= "2007-01-01" & Date <= "2017-12-31")

nonnative_filtered_data <- nonnative_diversity_data_extend %>%
  filter(Date >= "2007-01-01" & Date <= "2017-12-31")

native_diversity_plot <- ggplot(native_filtered_data, aes(x = Date, group = SeasonGroup)) +
  geom_smooth(aes(y = Shannon, color = "Shannon")) +
  geom_smooth(aes(y = Richness/10, color = "Richness (scaled)")) +
  geom_smooth(aes(y = Simpson, color = "Simpson")) +
  labs(x = "Year", y = "Diversity") +
  ggtitle("Native Diversity Indeces Over Time")

# Print the plot
print(native_diversity_plot)

This code provides some useful data. In it, plot the Shannon diversity over time for Native fish, nonnative fish, and all fish. This can be used to compare the growth rates based on origin surrounding the 2012 drought.

all_fish_data <- filtered_data %>%
  select(Date, Shannon) %>%
  mutate(Type = "All Fish")

native_fish_data <- native_filtered_data %>%
  select(Date, Shannon) %>%
  mutate(Type = "Native Fish")

nonnative_fish_data <- nonnative_filtered_data %>%
  select(Date, Shannon) %>%
  mutate(Type = "Nonnative Fish")

combined_data <- rbind(all_fish_data, native_fish_data, nonnative_fish_data)

combined_diversity_plot <- ggplot(combined_data, aes(x = Date, y = Shannon, color = Type, group = Type)) +
  geom_smooth() +
  labs(x = "Year", y = "Shannon Diversity", color = "Type") +
  ggtitle("Shannon Diversity Over Time for Fish of Differing Origins")

# Print the plot
print(combined_diversity_plot)

This chunk plots the average body length over time, then further splits up the categories for measuring body length by native and nonnative.

# Calculate the average body length by year for all fish
average_length_yearly_all <- platte_fish_extended %>%
  group_by(Year) %>%
  summarize(AverageLength = mean(Length, na.rm = TRUE)) %>%
  ungroup()

# Plot the average body length across time for all fish
ggplot(average_length_yearly_all, aes(x = Year, y = AverageLength)) +
  geom_smooth() +  # Line plot
  geom_point() +  # Points for each year
  labs(title = "Average Body Length Over Time (All Fish)",
       x = "Year",
       y = "Average Body Length") +
  theme_minimal()

# Calculate the average body length by year for native and nonnative fish
average_length_yearly_grouped <- platte_fish_extended %>%
  group_by(Year, Origin) %>%
  summarize(AverageLength = mean(Length, na.rm = TRUE)) %>%
  ungroup()

# Plot the average body length across time for native and nonnative fish
ggplot(average_length_yearly_grouped, aes(x = Year, y = AverageLength, color = Origin)) +
  geom_smooth() +  # Line plot
  geom_point() +  # Points for each year
  scale_x_continuous(breaks = unique(average_length_yearly_grouped$Year)) +  # Specify x-axis breaks to be each year
  labs(title = "Average Body Length Over Time by Fish Type",
       x = "Year",
       y = "Average Body Length",
       color = "Type") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))  # Rotate x-axis labels for readability

#### Here, we separate out sturgeon, a group of native species, and plot this population across time. We also ran an analysis to easily determine how many of the endangered pallid sturgeon were found during tht entire sampling period.

platte_fish_cleaned <- platte_fish_cleaned %>%
  mutate(Count = as.numeric(as.character(Count)))

#separating out all the different types of sturegeon
sturgeon_data <- platte_fish_cleaned %>%
  filter(Species %in% c(20,24,26,27,28))

sturgeon_data <- sturgeon_data %>%
  filter(!is.na(Count))

# Summarize the data to get the total count per year (or per month if needed)
sturgeon_count_yearly <- sturgeon_data %>%
  group_by(Year) %>%
  summarize(Total_Count = sum(Count, na.rm = TRUE)) %>%
  ungroup()

# Plot the Sturgeon count over time
ggplot(sturgeon_count_yearly, aes(x = Year, y = Total_Count)) +
  geom_smooth(color = "brown") +
  #geom_line() +  # Add lines connecting the points
  #geom_point(color = "brown") +  # Add points
  scale_x_continuous(breaks = unique(sturgeon_count_yearly$Year)) +  # Specify x-axis breaks to be each year
  labs(title = "Sturgeon Populations Over Time",
       x = "Year",
       y = "Total Count") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))  # Rotate x-axis labels for readability

sturgeon_min_length <- min(sturgeon_data$Length, na.rm = TRUE)
sturgeon_max_length <- max(sturgeon_data$Length, na.rm = TRUE)
sturgeon_mean_length <- mean(sturgeon_data$Length, na.rm = TRUE)
sturgeon_min_weight <- min(sturgeon_data$Weight, na.rm = TRUE)
sturgeon_max_weight <- max(sturgeon_data$Weight, na.rm = TRUE)
sturgeon_mean_weight <- mean(sturgeon_data$Weight, na.rm = TRUE)

# Filter the data for species 26
pallid_data <- platte_fish_cleaned %>%
  filter(Species == 26)

# Count the number of times species 26 was found
num_occurrences <- nrow(pallid_data)

# Sum up the count of species 26
total_count_pallid<- sum(pallid_data$Count, na.rm = TRUE)

# Print the results
print(paste("Pallid Sturgeon was found", num_occurrences, "times with a total count of", total_count_pallid, "individuals."))

## [1] "Pallid Sturgeon was found 2 times with a total count of 1 individuals."

We were also curious about sturgeon body length, so we plotted that as well here.

# Calculate the average body length by year
average_length_yearly <- sturgeon %>%
  group_by(Year) %>%
  summarize(AverageLength = mean(Length, na.rm = TRUE)) %>%
  ungroup()

# Plot the average body length across time
ggplot(average_length_yearly, aes(x = Year, y = AverageLength)) +
  geom_smooth(color = "brown") +  # Line plot
  #geom_point() +  # Points for each year
  scale_x_continuous(breaks = unique(average_length_yearly$Year)) +  # Specify x-axis breaks to be each year
  labs(title = "Average Sturgeon Body Length Over Time",
       x = "Year",
       y = "Average Body Length (mm)") +
  theme_minimal()

Here we plot the catfish population over time.

platte_fish_cleaned <- platte_fish_cleaned %>%
  mutate(Count = as.numeric(as.character(Count)))

#separates just the catfish
catfish_data <- platte_fish_cleaned %>%
  filter(Species %in% c(300,350,355,360,365,370))

catfish_data <- catfish_data %>%
  filter(!is.na(Count))

# Summarize the data to get the total count per year (or per month if needed)
catfish_count_yearly <- catfish_data %>%
  group_by(Year) %>%
  summarize(Total_Count = sum(Count, na.rm = TRUE)) %>%
  ungroup()

# Plot the catfish count over time
ggplot(catfish_count_yearly, aes(x = Year, y = Total_Count)) +
  geom_smooth() +
  #geom_line() +  # Add lines connecting the points
  #geom_point(color = "blue") +  # Add points
  scale_x_continuous(breaks = unique(catfish_count_yearly$Year)) +  # Specify x-axis breaks to be each year
  labs(title = "Catfish Population Over Time",
       x = "Year",
       y = "Total Count") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))  # Rotate x-axis labels for readability

Similarly, we plotted the average catfish body size.

platte_fish_cleaned <- platte_fish_cleaned %>%
  mutate(Count = as.numeric(as.character(Count)))

#separates just the catfish
catfish_data <- platte_fish_cleaned %>%
  filter(Species %in% c(300,350,355,360,365,370))

catfish_data <- catfish_data %>%
  filter(!is.na(Count))

# Summarize the data to get the total count per year (or per month if needed)
catfish_count_yearly <- catfish_data %>%
  group_by(Year) %>%
  summarize(Total_Count = sum(Count, na.rm = TRUE)) %>%
  ungroup()

# Plot the catfish count over time
ggplot(catfish_count_yearly, aes(x = Year, y = Total_Count)) +
  geom_smooth() +
  #geom_line() +  # Add lines connecting the points
  #geom_point(color = "blue") +  # Add points
  scale_x_continuous(breaks = unique(catfish_count_yearly$Year)) +  # Specify x-axis breaks to be each year
  labs(title = "Catfish Population Over Time",
       x = "Year",
       y = "Total Count") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))  # Rotate x-axis labels for readability

We are interested in non-native fish response to drought, so here we plotted carp population over time.

platte_fish_cleaned <- platte_fish_cleaned %>%
  mutate(Count = as.numeric(as.character(Count)))

#isolates just the carp
carp_data <- platte_fish_cleaned %>%
  filter(Species %in% c(176,178,180,181,182))

carp_data <- carp_data %>%
  filter(!is.na(Count))

# Summarize the data to get the total count per year (or per month if needed)
carp_count_yearly <- carp_data %>%
  group_by(Year) %>%
  summarize(Total_Count = sum(Count, na.rm = TRUE)) %>%
  ungroup()

# Plot the catfish count over time
ggplot(carp_count_yearly, aes(x = Year, y = Total_Count)) +
  geom_smooth(color = "red") +  # Add lines connecting the points
  #geom_point(color = "black") +  # Add points
  scale_x_continuous(breaks = unique(carp_count_yearly$Year)) +  # Specify x-axis breaks to be each year
  labs(title = "Carp Population Over Time",
       x = "Year",
       y = "Total Count") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))  # Rotate x-axis labels for readability

This calculates and plot average body length for carp

# Calculate the average body length by year
carp_average_length_yearly <- carp %>%
  group_by(Year) %>%
  summarize(AverageLength = mean(Length, na.rm = TRUE)) %>%
  ungroup()

# Plot the average body length across time
ggplot(carp_average_length_yearly, aes(x = Year, y = AverageLength)) +
  geom_smooth(color = "red") +  # Line plot
  #geom_point() +  # Points for each year
  scale_x_continuous(breaks = unique(carp_average_length_yearly$Year)) +  # Specify x-axis breaks to be each year
  labs(title = "Average Carp Body Length Over Time",
       x = "Year",
       y = "Average Body Length (mm)") +
  theme_minimal()

These chunks calculate the carp, catfish, and sturgeon populations based on each section of the river that was sampled from.

platte_fish_cleaned <- platte_fish_cleaned %>%
  mutate(Count = as.numeric(as.character(Count)))

# Filter for only carp data
carp_data <- platte_fish_cleaned %>%
  filter(Species %in% c(176,178,180,181,182)) %>%
  filter(!is.na(Count)) %>% 
  filter(Segment != "ELK", Segment != "PMC")

# Summarize the data to get the total count per segment per year
carp_count_segment_yearly <- carp_data %>%
  group_by(Segment, Year) %>%
  summarize(Total_Count = sum(Count, na.rm = TRUE)) %>%
  ungroup()

# Plot the Carp count over time for each segment
ggplot(carp_count_segment_yearly, aes(x = Year, y = Total_Count, group = Segment, color = Segment)) +
  geom_smooth() +  # Add lines connecting the points
  #geom_point() +  # Add points
  scale_x_continuous(breaks = unique(carp_count_segment_yearly$Year)) +  # Specify x-axis breaks to be each year
  labs(title = "Carp Populations Over Time by Segment",
       x = "Year",
       y = "Total Count") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))  # Rotate x-axis labels for readability

platte_fish_cleaned <- platte_fish_cleaned %>%
  mutate(Count = as.numeric(as.character(Count)))

#separates just the catfish
catfish_data <- platte_fish_cleaned %>%
  filter(Species %in% c(300,350,355,360,365,370))

catfish_data <- catfish_data %>%
  filter(!is.na(Count), !is.na(Segment)) %>% 
  filter(Segment != "ELK", Segment != "PMC")

# Summarize the data to get the total count per segment per year
catfish_count_segment_yearly <- catfish_data %>%
  group_by(Segment, Year) %>%
  summarize(Total_Count = sum(Count, na.rm = TRUE)) %>%
  ungroup()

# Plot the Carp count over time for each segment
ggplot(catfish_count_segment_yearly, aes(x = Year, y = Total_Count, group = Segment, color = Segment)) +
  geom_smooth() +  # Add lines connecting the points
  #geom_point() +  # Add points
  scale_x_continuous(breaks = unique(catfish_count_segment_yearly$Year)) +  # Specify x-axis breaks to be each year
  labs(title = "Catfish Populations Over Time by Segment",
       x = "Year",
       y = "Total Count") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))  # Rotate x-axis labels for readability

platte_fish_cleaned <- platte_fish_cleaned %>%
  mutate(Count = as.numeric(as.character(Count)))

#separating out all the differet types of sturegeon
sturgeon_data <- platte_fish_cleaned %>%
  filter(Species %in% c(20,24,26,27,28))

sturgeon_data <- sturgeon_data %>%
  filter(!is.na(Count)) %>% 
  filter(Segment != "ELK", Segment != "PMC", Segment != "CP")

# Summarize the data to get the total count per year (or per month if needed)
sturgeon_count_yearly_segment <- sturgeon_data %>%
  group_by(Segment, Year) %>%
  summarize(Total_Count = sum(Count, na.rm = TRUE)) %>%
  ungroup()

# Plot the Sturgeon count over time
ggplot(sturgeon_count_yearly_segment, aes(x = Year, y = Total_Count, group = Segment, color = Segment)) +
  geom_smooth() +  # Add lines connecting the points
  scale_x_continuous(breaks = unique(sturgeon_count_yearly$Year)) +  # Specify x-axis breaks to be each year
  labs(title = "Sturgeon Populations Over Time per Segment",
       x = "Year",
       y = "Total Count") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))  # Rotate x-axis labels for readability

This chunk plots shiner populations at each segment of the river from which they were sampled.

#separating out all the differet types of sturegeon
shiner_data <- platte_fish_cleaned %>%
  filter(Species %in% c(124, 126, 128, 129, 130, 132, 134, 138, 144, 145, 148, 149))

shiner_data <- shiner_data %>%
  filter(!is.na(Count)) 
  #filter(Segment != "ELK", Segment != "PMC", Segment != "CP")

# Summarize the data to get the total count per year (or per month if needed)
shiner_count_yearly <- shiner_data %>%
  group_by(Segment, Year) %>%
  summarize(Total_Count = sum(Count, na.rm = TRUE)) %>%
  ungroup()

# Plot the Sturgeon count over time
ggplot(shiner_count_yearly, aes(x = Year, y = Total_Count, group = Segment, color = Segment)) +
  geom_smooth() +  # Add lines connecting the points
  geom_point() +  # Add points
  scale_x_continuous(breaks = unique(shiner_count_yearly$Year)) +  # Specify x-axis breaks to be each year
  labs(title = "Shiner Populations Over Time per Segment",
       x = "Year",
       y = "Total Count") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))  # Rotate x-axis labels for readability

This chunk plots shiner populations over time. Then I find the range of body size, useful for my paper.

platte_fish_cleaned <- platte_fish_cleaned %>%
  mutate(Count = as.numeric(as.character(Count)))

#isolates just the shiner
shiner_data_avg <- platte_fish_cleaned %>%
  filter(Species %in% c(124, 126, 128, 129, 130, 132, 134, 138, 144, 145, 148, 149))

shiner_data_avg <- shiner_data_avg %>%
  filter(!is.na(Count))

# Summarize the data to get the total count per year (or per month if needed)
shiner_count_yearly <- shiner_data_avg %>%
  group_by(Year) %>%
  summarize(Total_Count = sum(Count, na.rm = TRUE)) %>%
  ungroup()

# Plot the shiner count over time
ggplot(shiner_count_yearly, aes(x = Year, y = Total_Count)) +
  geom_smooth(color = "orange") +  # Add lines connecting the points
#geom_point(color = "black") +  # Add points
  scale_x_continuous(breaks = unique(shiner_count_yearly$Year)) +  # Specify x-axis breaks to be each year
  labs(title = "Shiner Population Over Time",
       x = "Year",
       y = "Total Count") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))  # Rotate x-axis labels for readability

shiner_min_length <- min(shiner_data$Length, na.rm = TRUE)
shiner_max_length <- max(shiner_data$Length, na.rm = TRUE)
shiner_mean_length <- mean(shiner_data$Length, na.rm = TRUE)
shiner_min_weight <- min(shiner_data$Weight, na.rm = TRUE)
shiner_max_weight <- max(shiner_data$Weight, na.rm = TRUE)
shiner_mean_weight <- mean(shiner_data$Weight, na.rm = TRUE)

print(paste("The range of body length (mm) for the selected species is from", shiner_min_length, "to", shiner_max_length))

## [1] "The range of body length (mm) for the selected species is from 5 to 650"

print(paste("The range of body weight (g) for the selected species is from", shiner_min_weight, "to", shiner_max_weight))

## [1] "The range of body weight (g) for the selected species is from 0.3 to 1399"

This plots chub populations over time.

platte_fish_cleaned <- platte_fish_cleaned %>%
  mutate(Count = as.numeric(as.character(Count)))

#isolates just the chub
chub_data_avg <- platte_fish_cleaned %>%
  filter(Species %in% c(106, 112, 116, 118))

chub_data_avg <- chub_data_avg %>%
  filter(!is.na(Count))

# Summarize the data to get the total count per year (or per month if needed)
chub_count_yearly <- chub_data_avg %>%
  group_by(Year) %>%
  summarize(Total_Count = sum(Count, na.rm = TRUE)) %>%
  ungroup()

# Plot the chub count over time
ggplot(chub_count_yearly, aes(x = Year, y = Total_Count)) +
  geom_smooth() +  # Add lines connecting the points
  #geom_point(color = "blue") +  # Add points
  scale_x_continuous(breaks = unique(chub_count_yearly$Year)) +  # Specify x-axis breaks to be each year
  labs(title = "Chub Population Over Time",
       x = "Year",
       y = "Total Count") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))  # Rotate x-axis labels for readability

This plots minnow populations over time.

platte_fish_cleaned <- platte_fish_cleaned %>%
  mutate(Count = as.numeric(as.character(Count)))

#isolates just the minnow
minnow_data_avg <- platte_fish_cleaned %>%
  filter(Species %in% c(100, 142, 150, 152, 154, 156, 158, 160, 162))

minnow_data_avg <- minnow_data_avg %>%
  filter(!is.na(Count))

# Summarize the data to get the total count per year (or per month if needed)
minnow_count_yearly <- minnow_data_avg %>%
  group_by(Year) %>%
  summarize(Total_Count = sum(Count, na.rm = TRUE)) %>%
  ungroup()

# Plot the minnow count over time
ggplot(minnow_count_yearly, aes(x = Year, y = Total_Count)) +
  geom_smooth(color = "brown") +  # Add lines connecting the points
  #geom_point(color = "blue") +  # Add points
  scale_x_continuous(breaks = unique(minnow_count_yearly$Year)) +  # Specify x-axis breaks to be each year
  labs(title = "Minnow Population Over Time",
       x = "Year",
       y = "Total Count") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))  # Rotate x-axis labels for readability

platte_fish_cleaned <- platte_fish_cleaned %>%
  mutate(Count = as.numeric(as.character(Count)))

#isolates just the sunfish
sunfish_data_avg <- platte_fish_cleaned %>%
  filter(Species %in% c(700, 720, 722, 724, 726, 730, 738))

sunfish_data_avg <- sunfish_data_avg %>%
  filter(!is.na(Count))

# Summarize the data to get the total count per year (or per month if needed)
sunfish_count_yearly <- sunfish_data_avg %>%
  group_by(Year) %>%
  summarize(Total_Count = sum(Count, na.rm = TRUE)) %>%
  ungroup()

# Plot the sunfish count over time
ggplot(sunfish_count_yearly, aes(x = Year, y = Total_Count)) +
  geom_smooth() +  # Add lines connecting the points
  #geom_point(color = "blue") +  # Add points
  scale_x_continuous(breaks = unique(sunfish_count_yearly$Year)) +  # Specify x-axis breaks to be each year
  labs(title = "Sunfish Population Over Time",
       x = "Year",
       y = "Total Count") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))  # Rotate x-axis labels for readability

This plots crappie population over time. (Crappie used as a noun, not an adjective)

platte_fish_cleaned <- platte_fish_cleaned %>%
  mutate(Count = as.numeric(as.character(Count)))

#isolates just the carp
crappie_data_avg <- platte_fish_cleaned %>%
  filter(Species %in% c(780, 785, 790))

crappie_data_avg <- crappie_data_avg %>%
  filter(!is.na(Count))

# Summarize the data to get the total count per year (or per month if needed)
crappie_count_yearly <- crappie_data_avg %>%
  group_by(Year) %>%
  summarize(Total_Count = sum(Count, na.rm = TRUE)) %>%
  ungroup()

# Plot the catfish count over time
ggplot(crappie_count_yearly, aes(x = Year, y = Total_Count)) +
  geom_smooth() +  # Add lines connecting the points
  #geom_point(color = "blue") +  # Add points
  scale_x_continuous(breaks = unique(crappie_count_yearly$Year)) +  # Specify x-axis breaks to be each year
  labs(title = "Crappie Population Over Time",
       x = "Year",
       y = "Total Count") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))  # Rotate x-axis labels for readability

This shows the population of Gar in the Platte. This is of special interest due to the fact that Gar enjoy shallow, slow moving, warm water, and can tolerate low levels of dissolved oxygen.

platte_fish_cleaned <- platte_fish_cleaned %>%
  mutate(Count = as.numeric(as.character(Count)))

#isolates just the gar
gar_data_avg <- platte_fish_cleaned %>%
  filter(Species %in% c(42, 44))

gar_data_avg <- gar_data_avg %>%
  filter(!is.na(Count))

# Summarize the data to get the total count per year (or per month if needed)
gar_count_yearly <- gar_data_avg %>%
  group_by(Year) %>%
  summarize(Total_Count = sum(Count, na.rm = TRUE)) %>%
  ungroup()

# Plot the gar count over time
ggplot(gar_count_yearly, aes(x = Year, y = Total_Count)) +
  geom_smooth() +  # Add lines connecting the points
  #geom_point(color = "blue") +  # Add points
  scale_x_continuous(breaks = unique(gar_count_yearly$Year)) +  # Specify x-axis breaks to be each year
  labs(title = "Gar Population Over Time",
       x = "Year",
       y = "Total Count") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))  # Rotate x-axis labels for readability

This plots sucker populations over time. (Again, using sucker as a noun, not an adjective. I have no personal beef with these fish.)

platte_fish_cleaned <- platte_fish_cleaned %>%
  mutate(Count = as.numeric(as.character(Count)))

#isolates just the carp
sucker_data_avg <- platte_fish_cleaned %>%
  filter(Species %in% c(210, 212, 214, 224, 230))

sucker_data_avg <- sucker_data_avg %>%
  filter(!is.na(Count))

# Summarize the data to get the total count per year (or per month if needed)
sucker_count_yearly <- sucker_data_avg %>%
  group_by(Year) %>%
  summarize(Total_Count = sum(Count, na.rm = TRUE)) %>%
  ungroup()

# Plot the catfish count over time
ggplot(sucker_count_yearly, aes(x = Year, y = Total_Count)) +
  geom_smooth(color = "purple") +  # Add lines connecting the points
  #geom_point(color = "purple") +  # Add points
  scale_x_continuous(breaks = unique(sucker_count_yearly$Year)) +  # Specify x-axis breaks to be each year
  labs(title = "Sucker Population Over Time",
       x = "Year",
       y = "Total Count") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))  # Rotate x-axis labels for readability

This is the real gem of these analyses. This code scales and then plots native and nonnative populations over time. It is scaled so that their qualitative trajectories can be compared.

# Summarize native fish data by year
native_fish_yearly <- native_fish %>%
  group_by(Year) %>%
  summarise(TotalCount = sum(Count, na.rm = TRUE))

# Summarize non-native fish data by year
nonnative_fish_yearly <- nonnative_fish %>%
  group_by(Year) %>%
  summarise(TotalCount = sum(Count, na.rm = TRUE))

# Combine the datasets
combined_fish_data <- bind_rows(
  mutate(native_fish_yearly, Origin = "Nat"),
  mutate(nonnative_fish_yearly, Origin = "Non")
)

# Find the maximum count for scaling
max_count_native <- max(native_fish_yearly$TotalCount)
max_count_nonnative <- max(nonnative_fish_yearly$TotalCount)
# Create the plot
plot <- ggplot() +

  geom_line(data = combined_fish_data[combined_fish_data$Origin == "Nat", ], 
            aes(x = Year, y = TotalCount/max_count_native, group = Origin), 
            color = "blue") +
  scale_x_continuous(breaks = unique(native_fish_yearly$Year)) +  # Specify x-axis breaks to be each year
  geom_point() +

  geom_line(data = combined_fish_data[combined_fish_data$Origin == "Non", ], 
            aes(x = Year, y = TotalCount/max_count_nonnative, group = Origin), 
            color = "red") +
  geom_point() +
  scale_x_continuous(breaks = unique(nonnative_fish_yearly$Year)) +  # Specify x-axis breaks to be each year
  labs(x = "Year", y = "Total Count (Scaled)", title = "Native and Nonnative Fish Population (Scaled)") +
  theme_minimal()

# Print the plot
print(plot)

This chunk combines native populations of interest and plots them all together.

# Add a 'Group' column to each dataset
minnow_count_yearly$Group <- 'Minnow'
shiner_count_yearly$Group <- 'Shiner'
sturgeon_count_yearly$Group <- 'Sturgeon'
sucker_count_yearly$Group <- 'Sucker'
gar_count_yearly$Group <- 'Gar'
catfish_count_yearly$Group <- 'Catfish'

# Combine the datasets
combined_data <- rbind(gar_count_yearly, minnow_count_yearly, shiner_count_yearly, catfish_count_yearly, sturgeon_count_yearly, sucker_count_yearly)

# Plot the combined data with separate panels for each group
ggplot(combined_data, aes(x = Year, y = Total_Count)) +
  geom_smooth(aes(color = Group)) +  # Add lines connecting the points
  #geom_point(aes(color = Group)) +  # Add points
  scale_x_continuous(breaks = unique(combined_data$Year)) +  # Specify x-axis breaks to be each year
  facet_wrap(~ Group, scales = "free_y") +  # Create separate panels for each group
  labs(title = "Population Over Time by Native Fish Group",
       x = "Year",
       y = "Total Count") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))  # Rotate x-axis labels for readability

Here, we are plotting native vs nonnative fish for each segment, CP, LLP, and ULP

create_segment_plot <- function(segment_data, title) {
  # Calculate max counts for scaling
  max_count_native <- max(segment_data[segment_data$Origin == "Nat",]$TotalCount, na.rm = TRUE)
  max_count_nonnative <- max(segment_data[segment_data$Origin == "Non",]$TotalCount, na.rm = TRUE)

  # Create the plot
  plot <- ggplot() +
    geom_smooth(data = segment_data[segment_data$Origin == "Nat", ], 
              aes(x = Year, y = TotalCount/max_count_native, group = Origin), 
              color = "blue") +
    scale_x_continuous(breaks = unique(nonnative_fish_yearly$Year)) +  # Specify x-axis breaks to be each year
    geom_smooth(data = segment_data[segment_data$Origin == "Non", ], 
              aes(x = Year, y = TotalCount/max_count_nonnative, group = Origin), 
              color = "red") +
    scale_y_continuous("Count (Scaled)", 
                       labels = percent_format()) +
    labs(x = "Year", y = "Total Count", title = title) +
    theme_minimal()

  return(plot)
}

# Aggregating data by year and origin for each segment
summarize_segment_data <- function(segment_data) {
  segment_data %>%
    group_by(Year, Origin) %>%
    summarise(TotalCount = sum(Count, na.rm = TRUE)) %>%
    ungroup()
}

LLP_fish_summary <- summarize_segment_data(LLP_fish)
CP_fish_summary <- summarize_segment_data(CP_fish)
ULP_fish_summary <- summarize_segment_data(ULP_fish)
ELK_fish_summary <- summarize_segment_data(ELK_fish)
PMC_fish_summary <- summarize_segment_data(PMC_fish)

# Create plots for each segment
LLP_plot <- create_segment_plot(LLP_fish_summary, "LLP Fish Counts Over Time by Origin")
CP_plot <- create_segment_plot(CP_fish_summary, "CP Fish Counts Over Time by Origin")
ULP_plot <- create_segment_plot(ULP_fish_summary, "ULP Fish Counts Over Time by Origin")
ELK_plot <- create_segment_plot(ELK_fish_summary, "ELK Fish Counts Over Time by Origin")
PMC_plot <- create_segment_plot(PMC_fish_summary, "PMC Fish Counts Over Time by Origin")

# Print the plots (or save them as needed)
print(LLP_plot)

print(CP_plot)

print(ULP_plot)

#print(ELK_plot)
#print(PMC_plot)

Here I’m calculating the Jaccard index, although it wasn’t used in the final paper. Still, this could be useful for future projects.

# Convert counts to presence/absence
fish_vegan_pa <- fish_vegan %>%
  mutate(across(-sample_id, ~ ifelse(. > 0, 1, 0)))  # Convert counts to 1 (presence) or 0 (absence) for all species columns

# Separate Segment from sample_id
fish_vegan_pa <- fish_vegan_pa %>%
  separate(sample_id, into = c("Segment", "Year", "Month", "Day"), sep = "-") %>%
  filter(!Segment %in% c("ELK", "PMC")) %>%
  select(-Year, -Month, -Day)  # Remove date parts if only segment comparison is needed

# Aggregate by Segment
fish_vegan_pa_aggregated <- fish_vegan_pa %>%
  group_by(Segment) %>%
  summarise(across(everything(), max))  # Aggregate by taking the maximum (presence/absence) across all species

jaccard_numeric_data <- fish_vegan_pa_aggregated %>% select(-Segment)
# Now, 'fish_vegan_pa_aggregated' is ready for Jaccard index calculation

# Set row names to Segment names for Jaccard Distance calculation
row.names(jaccard_numeric_data) <- fish_vegan_pa_aggregated$Segment

## Warning: Setting row names on a tibble is deprecated.

# Calculate Jaccard Distance
jaccard_dist <- vegdist(jaccard_numeric_data, method = "jaccard")

# View the Jaccard Distance matrix
print(jaccard_dist)

##            CP       LLP
## LLP 0.4615385          
## ULP 0.3417722 0.3750000

# Convert the distance matrix to a format suitable for ggplot
melted_jaccard_dist <- melt(as.matrix(jaccard_dist))

# Create heatmap
ggplot(melted_jaccard_dist, aes(Var1, Var2, fill = value)) +
  geom_tile() +
  scale_fill_gradient(low = "white", high = "black") +
  labs(x = "Segment", y = "Segment", fill = "Jaccard Distance") +
  theme_minimal()

# 1. CP
# 2. ELK
# 3. LLP
# 4. PMC
# 5. ULP
# let's try to remove 4 and 2, aka ELK and PMC. 

This chunk uses reads data collected by the Nebraska Department of Resources and plots both the temperature and the flow rate across our year of interest: 2012 (the year of the drought).

#retrieves Louisville  info
site_number <- c("06805500")
site_info <- readNWISsite(site_number)
#print(site_info)

#daily value available for Louisville
dailyDataAvailable <- whatNWISdata(
  siteNumber = site_number,
  service = "dv",
  statCd = "00003"
)

#print(dailyDataAvailable)

#reads water usage statistics
allegheny <- readNWISuse(
  stateCd = "Nebraska",
  countyCd = NULL
)
#print(allegheny)

parameterCd <- c("00010", "00060") # Temperature and discharge
statCd <- c("00001", "00003") # Mean and maximum
startDate <- "2011-01-01"
endDate <- "2014-01-01"

temperatureAndFlow <- readNWISdv(site_number, parameterCd,
  startDate, endDate,
  statCd = statCd
)
temperatureAndFlow <- renameNWISColumns(temperatureAndFlow)
names(temperatureAndFlow)

## [1] "agency_cd"    "site_no"      "Date"         "Wtemp_Max"    "Wtemp_Max_cd"
## [6] "Wtemp"        "Wtemp_cd"     "Flow"         "Flow_cd"

names(attributes(temperatureAndFlow))

## [1] "names"         "row.names"     "url"           "siteInfo"     
## [5] "variableInfo"  "disclaimer"    "statisticInfo" "queryTime"    
## [9] "class"

statInfo <- attr(temperatureAndFlow, "statisticInfo")
variableInfo <- attr(temperatureAndFlow, "variableInfo")
siteInfo <- attr(temperatureAndFlow, "siteInfo")




# Plotting the data
variableInfo <- attr(temperatureAndFlow, "variableInfo")
siteInfo <- attr(temperatureAndFlow, "siteInfo")


par(mar = c(5, 5, 5, 5)) # sets the size of the plot window

plot(temperatureAndFlow$Date, temperatureAndFlow$Wtemp_Max,
  ylab = "Temperature (deg C)",
  xlab = "",
  pch = 1, # Sets the type of point
  col = "black"
)
par(new = TRUE)
temp_flow_plot <- plot(temperatureAndFlow$Date,
  temperatureAndFlow$Flow,
  col = "blue", type = "l",
  xaxt = "n", yaxt = "n",
  xlab = "", ylab = "",
  axes = FALSE
)
axis(4, col = "blue", col.axis = "blue")
mtext("Flow (ft3/s)", side = 4, line = 3, col = "blue")
title(paste(siteInfo$station_nm, "2012"))

legend("topleft", 
       legend = c("Temp. (deg C)", "Flow (ft3/s)"), 
       col = c("black", "blue"), 
       lty = c(NA, 1), # Only second item (flow) has a line type
       pch = c(1, NA)  # Only first item (temperature) has a point type
)

png("temp_flow_plot.png", width = 8, height = 6, units = "in", res = 300)
par(mar = c(5, 5, 5, 5)) # sets the size of the plot window

plot(temperatureAndFlow$Date, temperatureAndFlow$Wtemp_Max,
  ylab = "Temperature (deg C)",
  xlab = "",
  pch = 1, # Sets the type of point
  col = "black"
)
par(new = TRUE)
temp_flow_plot <- plot(temperatureAndFlow$Date,
  temperatureAndFlow$Flow,
  col = "blue", type = "l",
  xaxt = "n", yaxt = "n",
  xlab = "", ylab = "",
  axes = FALSE
)
axis(4, col = "blue", col.axis = "blue")
mtext("Flow (ft3/s)", side = 4, line = 3, col = "blue")
title(paste(siteInfo$station_nm, "2012"))

legend("topleft", 
       legend = c("Temp. (deg C)", "Flow (ft3/s)"), 
       col = c("black", "blue"), 
       lty = c(NA, 1), # Only second item (flow) has a line type
       pch = c(1, NA)  # Only first item (temperature) has a point type
)
dev.off()

## quartz_off_screen 
##                 2

This analysis is useful for our research, as it calculates various attributes from our data,

# Count the total number of nonnative fish
total_nonnative_fish <- sum(nonnative_fish$Count)
total_native_fish <- sum(native_fish$Count)
total_fish <- sum(platte_fish_cleaned$Count)




# Filter the rows where species is 178
species_178_data <- nonnative_fish[nonnative_fish$Species == 178, ]

# Sum the "Count" column of this filtered dataset
fish_178_count <- sum(species_178_data$Count)

# Print the result
print(paste("Total count of species 178:", fish_178_count))

## [1] "Total count of species 178: 2658"

# Calculate the percentage
percentage_178 <- (fish_178_count / total_fish) * 100

# Print the result
print(paste("Percentage of Common Carp:", percentage_178, "%"))

## [1] "Percentage of Common Carp: 4.54001981348 %"

# Filter the rows where species is 178
channel_cat_data <- native_fish[native_fish$Species == 360, ]

# Sum the "Count" column of this filtered dataset
channel_cat_count <- sum(channel_cat_data$Count)

# Print the result
print(paste("Total count of channel catfish:", channel_cat_count))

## [1] "Total count of channel catfish: 20575"

# Calculate the percentage
percentage_channel_cat <- (channel_cat_count / total_fish) * 100

# Print the result
print(paste("Percentage of Channel Catfish:", percentage_channel_cat, "%"))

## [1] "Percentage of Channel Catfish: 35.143306118266 %"

# Filter the rows where species is 178
species_carp_data <- nonnative_fish[nonnative_fish$Species %in% c(176,178,180,181,182), ]

# Sum the "Count" column of this filtered dataset
species_carp_count <- sum(species_carp_data$Count)

# Print the result
print(paste("Total count of carps:", species_carp_count))

## [1] "Total count of carps: 2819"

# Calculate the percentage
carp_percentage <- (species_carp_count / total_fish) * 100

# Print the result
print(paste("Percentage of Carp:", carp_percentage, "%"))

## [1] "Percentage of Carp: 4.81501725139207 %"

# Filter the rows where species is 178
species_catfish_data <- native_fish[native_fish$Species %in% c(300,350,355,360,365,370), ]

# Sum the "Count" column of this filtered dataset
species_catfish_count <- sum(species_catfish_data$Count)

# Print the result
print(paste("Total count of catfish:", species_catfish_count))

## [1] "Total count of catfish: 22337"

# Calculate the percentage
catfish_percentage <- (species_catfish_count / total_fish) * 100

# Print the result
print(paste("Percentage of catfish:", catfish_percentage, "%"))

## [1] "Percentage of catfish: 38.1529054077136 %"

# Filter the rows where species is 178
species_sturgeon_data <- native_fish[native_fish$Species %in% c(26,28), ]

# Sum the "Count" column of this filtered dataset
species_sturgeon_count <- sum(species_sturgeon_data$Count)

# Print the result
print(paste("Total count of sturgeon:", species_sturgeon_count))

## [1] "Total count of sturgeon: 170"

# Calculate the percentage
sturgeon_percentage <- (species_sturgeon_count / total_fish) * 100

# Print the result
print(paste("Percentage of sturgeon:", sturgeon_percentage, "%"))

## [1] "Percentage of sturgeon: 0.290369965497216 %"

# List of electrofishing gear types
electrofishing_gear <- c("EF", "BPEF", "EFH", "EFL", "FAC")

# Filter the rows where gear is one of the electrofishing types
electrofishing_data <- platte_fish_cleaned[platte_fish_cleaned$Gear %in% electrofishing_gear, ]

# Sum the "Count" column of this filtered dataset
electrofishing_count <- sum(electrofishing_data$Count)


# Calculate the percentage
percentage_electrofishing <- (electrofishing_count / total_fish) * 100







# Print the result
print(paste("Percentage of fish collected using electrofishing methods:", round(percentage_electrofishing, 2), "%"))

## [1] "Percentage of fish collected using electrofishing methods: 69.02 %"

species_names <- data.frame(SpeciesID = c(360, 120, 144, 178, 370, 212, 132, 862, 262, 44))
                            SpeciesName = c("Channel Catfish", "Red Shiner", "Sand Shiner", "Common Carp", "Flathead Catfish", "River Carpsucker", "River Shiner, Freshwater drum, Shorthead Redhorse", "Shortnose Gar")



# Count the occurrences of each species ID
species_counts <- table(platte_fish_cleaned$Species)

# Convert to a data frame
species_df <- as.data.frame(species_counts, stringsAsFactors = FALSE)
names(species_df) <- c("SpeciesID", "Count")

# Merge the species names with species_df
species_df <- merge(species_df, species_names, by = "SpeciesID", all.x = TRUE)

# Sort the data frame by count in descending order
species_df <- species_df[order(-species_df$Count), ]

# Optionally, add a rank column
species_df$Rank <- 1:nrow(species_df)

# View the top species
head(species_df)

##    SpeciesID Count Rank
## 49       360 20504    1
## 7        120  9229    2
## 18       144  3031    3
## 30       178  2660    4
## 51       370  1705    5
## 35       212  1536    6

Finally, we combine the native and nonnative fish populations into one plot.

#ggsave("temp_flow_plot.png", temp_flow_plot, width = 8, height = 6)
#ggsave("LLP_plot.png", LLP_plot, width = 8, height = 6)
#ggsave("CP_plot.png", CP_plot, width = 8, height = 6)
#ggsave("ULP_plot.png", ULP_plot, width = 8, height = 6)

# Read the images
img1 <- image_read("temp_flow_plot.png")
img2 <- image_read("LLP_plot.png")
img3 <- image_read("CP_plot.png")
img4 <- image_read("ULP_plot.png")
# Add others as needed

# Combine images (adjust the layout as needed)
combined_image <- image_montage(c(img1, img2, img3, img4), tile = "2x2")

# Save the combined image
image_write(combined_image, "combined_plots.png", format = "png")

# Display the combined image in R Markdown
include_graphics("combined_plots.png")